Metabolic Assessment in Non-Dialysis Patients with Chronic Kidney Disease Using LC-MS

In a recent study published in the Journal of Inflammation Research (1), plasma samples from 47 patients with stages 1– 4 chronic kidney disease (CKD) not requiring dialysis (as well as 30 healthy controls) were analyzed with liquid chromatography-mass spectrometry (LC-MS) to investigate the changes of different metabolites in the body fluids of non-dialysis patients with CKD using a metabolomics approach. The goal of this study was to identify early biomarkers of CKD progression through metabolic pathway analysis.

CKD is a prevalent condition in the general population, and it has become a significant global public health concern, with the estimated global prevalence of CKD ranging from 10.6% to 13.4% (2). In the early stages of the disease, the symptoms may not be obvious, and therefore can be easily overlooked. The progression of CKD towards end-stage renal disease (ESRD), however, is unfortunately associated with high costs, mortality, and a significant decline in a patient’s quality of life, particularly when dialysis treatment is introduced. The plasma biochemical marker of glomerular filtration rate (GFR), plasma creatinine concentration (sCr), though widely used, is often considered insensitive for detecting early stages of CKD. Therefore, the early recognition of CKD is crucial in slowing down disease progression, reducing morbidity, and improving survival.

Metabolomics, a systematic analysis of metabolites in a biological specimen, is focused on the dynamic changes, interactions, and responses of metabolites in various metabolic pathways, and has become a commonly used approach in systems biology research, especially in the identification of new diagnostic and prognostic biomarkers for human diseases (3,4).

While the authors of the study state that LC-MS is a powerful tool that enables accurate identification and quantification of compounds due to its greater sensitivity and specificity, the technique’s current utilization is primarily involved with the uncovering of the complexity of plasma metabolome and the provision of various biomedical applications based on the obtained results. The primary objective of their study, then, was to assess plasma metabolic status in non-dialysis patients with early CKD compared with controls using LC-MS.

Plasma samples from CKD patients and controls were successfully differentiated using an orthogonal least squares discriminant analysis model (OPLS-DA) model. Twenty-five compounds were initially identified as potential plasma metabolic markers for distinguishing CKD patients from healthy controls. Among these, six compounds (ADMA, D-ornithine, kynurenine, kynurenic acid, 5-hydroxyindoleacetic acid, and gluconic acid) were found to be associated with CKD progression. Changes in metabolic pathways associated with CKD progression include arginine and ornithine metabolism, tryptophan metabolism, and the pentose phosphate pathway.

By analyzing the metabolic pathways of different metabolites, the researchers have identified the significant impact of CKD progression. The main metabolic pathways involved are arginine and ornithine metabolism, tryptophan metabolism, and pentose phosphate pathway. ADMA, D-ornithine, L-kynurenine, kynurenic acid, 5-hydroxyindoleacetic acid, and gluconic acid could serve as potential early biomarkers for CKD progression. The researchers said that their findings offer the possibility of early CKD detection in patients, and the exploration of intervention utilizing common clinical drugs targeting these metabolic pathways and metabolites offer promise for the treatment of the disease.

Human kidney cross section on scientific background. © Crystal light - stock.adobe.com

References

1. Hong, H.; Zhou, S.; Zheng, J.; Shi, H.; Chen, Y.; Li, M. Metabolic Assessment in Non-Dialysis Patients with Chronic Kidney Disease. J. Inflamm. Res. 2024, 17, 5521–5531. DOI: 10.2147/JIR.S461621

2. Hill, N. R.; Fatoba, S. T.; Oke, J. L. Global Prevalence of Chronic Kidney Disease – A Systematic Review and Meta-Analysis. PLoS One 2016, 11 (7), e0158765. DOI:10.1371/journal.pone.0158765

3. Levey, A. S.; Stevens, L. A.; Schmid, C. H. et al. A New Equation to Estimate Glomerular Filtration Rate. Ann. Intern Med. 2009, 150, 604. DOI: 10.7326/0003-4819-150-9-200905050-00006

4. Zhang, Z. H.; Chen, H.; Vaziri, N.D. et al. Metabolomic Signatures of Chronic Kidney Disease of Diverse Etiologies in the Rats and Humans. J Proteome Res. 2016;15(10):3802–3812. DOI:10.1021/acs.jproteome.6b00583